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Due to the inhomogeneity of the medium and the scattering effect, phase distortion and intensity attenuation occur during the propagation of light, leading to the inability to obtain clear imaging of the target. Adaptive optics technology can compensate for the influence of scattering media on incident light waves by controlling the phase and amplitude distribution of the incident light. However, existing adaptive optics methods require prior knowledge of the target scene, which to some extent limits the application scenarios of this technology. To address this issue, this study constructs a no-reference image quality assessment system as a fitness metric. It iteratively generates the optimal compensating phase using a Genetic Algorithm(GA), enabling clear imaging of hidden targets in situations where the target scene information is unknown and there are no guide stars. Experimental results demonstrate that the employed no-reference image quality assessment system effectively constrains the optimization process. Specifically, the Energy gradient and Brenner gradient exhibit significant constraint effects in the early stages of evolution, showing a logarithmic improvement in imaging quality. The Tenengrad gradient performs best in the later convergence stage, achieving a peak signal-to-noise ratio(PSNR) of 14.34dB.
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